AI-Driven Intelligent Aerospace Monitoring and Real-Time Update System for Predictive Decision Making

Abstract

The aerospace industry operates in a highly dynamic and safety-critical environment where real-time decision-making is essential for ensuring operational efficiency and minimizing risks. Traditional monitoring systems often rely on reactive maintenance and limited data integration, leading to inefficiencies and potential safety concerns. This paper presents an AI-driven intelligent aerospace monitoring and real-time update system designed to enhance predictive decision-making capabilities. The proposed system integrates heterogeneous data sources, including aircraft sensors, weather information, and satellite telemetry, into a unified framework. Advanced machine learning algorithms are employed for anomaly detection, predictive maintenance, and operational optimization. A real-time dashboard provides actionable insights and alerts to operators. Experimental evaluation demonstrates improved fault prediction accuracy, reduced downtime, and enhanced situational awareness. The system offers a scalable and efficient solution for modern aerospace operations.